AI and machine learning
Training, fine-tuning, retrieval, agents — and knowing when not to.
Four to sixteen weeks.
Train or fine-tune a model on your own data
We build the evaluation harness before touching a model, so better is a number rather than an impression. Then dataset construction — usually the part that decides the outcome — followed by a full fine-tune, LoRA or QLoRA, or preference tuning, whichever the task and the budget justify.
The problem
A general-purpose model does not know your domain, your terminology or your documents, and no amount of prompt engineering is fixing it.
How we approach it
We build the evaluation harness before touching a model, so better is a number rather than an impression. Then dataset construction — usually the part that decides the outcome — followed by a full fine-tune, LoRA or QLoRA, or preference tuning, whichever the task and the budget justify.
What changes
A model measurably better than the baseline on the cases you care about, an evaluation harness you keep and can re-run, and a deployment you own — hosted, self-hosted or on-device.
The service this falls under
Industries where this comes up
Software and technology companies
Security review, automated usability testing, vulnerability scanning in CI, and the edge and bot policy that keeps a product reachable but not scrapeable.
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